✨ A plotter for reinforcement learning (RL)

📉 Visualize your Deep Learning training in static graphics

All about machine learning

Usages Examples for ML-Logger

Machine Learning analysis for an imbalanced dataset. Developed as final project for the course "Machine Learning and Intelligent Systems" at Eurecom, Sophia Antipolis

Implementation of Andrew Ng's machine learning exercises in Julia

Nine diagnostic tools for detecting and understanding overfitting in scikit-learn models — polynomial overfitting, learning curves, validation curves, bias-variance decomposition, regularisation sweeps, data leakage detection, and more. Companion code for the ML Diagnostics Mastery series.

An Implementation of Crawford's and Wright's Learning Curve Production Functions

Built a model to predict the value of a given house in the Boston real estate market using various statistical analysis tools. Identified the best price that a client can sell their house utilizing machine learning.

A new package that helps users compare and choose the right data analysis tool by providing structured, expert-level insights. Users input their specific data analysis needs, project requirements, or

Laptop price prediction with regression models, preprocessing, feature engineering, cross-validation, regularization, and learning-curve analysis. Compares linear, polynomial, ridge, lasso, and regression tree models using cross-validated evaluation. (Regression Task ML)

Using Alexnet Architecture for Multiclass Fruit Classification

Diabetes disease classification with preprocessing, PCA & PSO feature reduction, and SMOTE-based imbalance handling, along with cross-validation, hyperparameter tuning, and learning-curve analysis. Compares logistic regression, Gaussian Naive Bayes, AdaBoost, Random Forest, SVM, MLP, and XGBoost using cross-validated evaluation. (Task 2 ML)

Image processing with python and Machine learning. This is about analyzing Ayurveda plants and detection through uploading an image of plant leaves or flowers.

important code snippets in machine learning

Plotting cost curves of solar PV

Built a model to predict the value of a given house in the Boston real estate market using various statistical analysis tools. Identified the best price that a client can sell their house utilizing machine learning.

Simple Python tools for plotting learning curves of neural network models trained with the Keras, Ultralytics YOLO or scikit-learn framework in a single figure in an easy-to-understand format.

Interactive ML experimentation platform for understanding model behavior and statistical decision-making (A/B testing, bias-variance, learning curves).